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Classification of PolSAR Images Based on Adaptive Nonlocal stacked sparse autoencoder

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2018年第7期15卷 1050-1054页

作者： Hu, Yuanyuan Fan, Jianchao Wang, Jun Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Natl Marine Environm Monitoring Ctr Dalian 116023 Peoples R China

Land cover classification using polarimetric synthetic aperture radar (PolSAR) images is an important tool for remote sensing analysis. In view that PolSAR image effective interpretation is commonly affected by the absence of discriminative features and the presence of speckle noises, this letter proposes an adaptive nonlocal stacked sparse autoencoder (ANSSAE) to achieve PolSAR image classification. It extracts the adaptive nonlocal spatial information by adaptively calculating weighted average values of each pixel from nonlocal regions, which can reduce the influence of speckle noises and retain edge details. In the first layer of the ANSSAE, the adaptive nonlocal spatial information is introduced into the objective function to obtain the robust feature representation, whose effects would transfer to the rest of layers. Therefore, the ANSSAE can automatically capture spatial-related, robust, and distinguishable features, which can suppress speckle noises and gain accurate classification results. Experimental results on two real PolSAR images demonstrate that the proposed approach can significantly improve the classification accuracy.

关键词： Adaptive nonlocal spatial information polarimetric synthetic aperture radar (PolSAR) image classification stacked sparse autoencoder

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stacked sparse autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2016年第1期35卷 119-130页

作者： Xu, Jun Xiang, Lei Liu, Qingshan Gilmore, Hannah Wu, Jianzhong Tang, Jinghai Madabhushi, Anant Nanjing Univ Informat Sci & Technol Jiangsu Key Lab Big Data Anal Tech Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol CICAEET Nanjing 210044 Jiangsu Peoples R China Case Western Reserve Univ Univ Hosp Case Med Ctr Dept Pathol Anat Cleveland OH 44106 USA Jiangsu Canc Hosp Nanjing 210000 Jiangsu Peoples R China Case Western Reserve Univ Dept Biomed Engn Cleveland OH 44106 USA

Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens. The Nottingham Histologic Score system is highly correlated with the shape and appearance of breast cancer nuclei in histopathological images. However, automated nucleus detection is complicated by 1) the large number of nuclei and the size of high resolution digitized pathology images, and 2) the variability in size, shape, appearance, and texture of the individual nuclei. Recently there has been interest in the application of "Deep Learning" strategies for classification and analysis of big image data. Histopathology, given its size and complexity, represents an excellent use case for application of deep learning strategies. In this paper, a stacked sparse autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer. The SSAE learns high-level features from just pixel intensities alone in order to identify distinguishing features of nuclei. A sliding window operation is applied to each image in order to represent image patches via high-level features obtained via the auto-encoder, which are then subsequently fed to a classifier which categorizes each image patch as nuclear or non-nuclear. Across a cohort of 500 histopathological images (2200 2200) and approximately 3500 manually segmented individual nuclei serving as the groundtruth, SSAE was shown to have an improved F-measure 84.49% and an average area under Precision-Recall curve (AveP) 78.83%. The SSAE approach also out-performed nine other state of the art nuclear detection strategies.

关键词： Terms-Automated nuclei detection breast cancer histopathology feature representation learning stacked sparse autoencoder digital pathology deep learning

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Research on Visual Speech Recognition Based on Local Binary Pattern and stacked sparse autoencoder 1st

Research on Visual Speech Recognition Based on Local Binary ...

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1st International Conference on Human Systems Engineering and Design (IHSED) - Future Trends and Applications

作者： Lu, Yuanyao Gu, Ke He, Shan North China Univ Technol Sch Elect & Informat Engn Beijing 100144 Peoples R China

ISBN: (纸本)9783030020538;9783030020521

Lip feature extraction from human mouth image plays an essential role in visual speech recognition applications. This paper presents a lip feature extraction algorithm based on Local Binary Patterns (LBP) and stacked sparse autoencoders (SSAE). First, LBP texture features are extracted from lip images. Then SSAE uses greedy unsupervised learning to extract high-level features. At last, we improve the performance of overall system by fine-tuning and input the extracted features into the Softmax classifier. Compared with traditional methods, the model proposed in this paper has higher classification accuracy and more applicability.

关键词： Visual speech recognition Local Binary Pattern stacked sparse autoencoder

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Remaining Useful Life Prediction Based on stacked sparse autoencoder and Echo State Network 39

Remaining Useful Life Prediction Based on Stacked Sparse Aut...

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39th Chinese Control Conference (CCC)

作者： Yang, Yinghua Yao, Dandan Liu, Xiaozhi Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China

ISBN: (纸本)9789881563903

Nowadays, the prediction of industry components' remaining useful life (RUL) has already become a hot topic. In this paper, a RUL prediction method based on stacked sparse autoencoder (SAE) and echo state network (ESN) is proposed. autoencoder (AE) is an unsupervised learning method that can be used for feature extraction to obtain a health index (HI). To enhance the performance of the HI, a sparse constraint is added and a stacked structure is used to increase depth. The RUL of the industry components is then predicted using a method that maps directly to the RUL value based on the health factor curve. With the capability of encapsulating dynamic time behavior and saving historical information of input data, ESN is selected as the prediction network. The proposed method is verified using the C-MPASS dataset. The experimental results show that the method has better performance than the stacked AE based method.

关键词： Remaining useful life stacked sparse autoencoder echo state network

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Missing Structural and Clinical Features Imputation for Semi-supervised Alzheimer's Disease Classification using stacked sparse autoencoder

Missing Structural and Clinical Features Imputation for Semi...

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IEEE Biomedical Circuits and Systems Conference (BioCAS) - Advanced Systems for Enhancing Human Health

作者： Jabason, Emimal Ahmad, M. Omair Swamy, M. N. S. Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9781538636039

In recent years, the accurate detection of Alzheimer's disease (AD) at its early stage, using various biomarkers through machine learning techniques, has been given paramount importance in the medical field. However, in reality, the input datasets contain lots of missing values due to several factors such as increasing mortality rate, avoiding invasive procedures, and dropping out from the study. In this work, after analyzing the pattern of structural and clinical data from tadpole study in Alzheimer's disease neuroimaging initiative (ADNI) database, it has been found that the unobserved data are not missing completely at random. In view of this fact, with the assumption that the missing data patterns are in blocks, we propose a novel stacked sparse autoencoder based method to assign a value in the missing places and to select the significant structural and clinical features in order to discriminate the patients having AD, mild cognitive impairment (MCI), and cognitively normal (CN) clinical status. Through experimental results, it is shown that the proposed imputation algorithm achieves better performance for semi-supervised AD classification in terms of accuracy, sensitivity, and specificity in 5-fold cross validation when compared to the state-of-the-art methods.

关键词： Alzheimer's disease (AD) Structural magnetic resonance imaging (MRI) Neurophysiological test Imputation Feature selection stacked sparse autoencoder Supervised classification

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Application of stacked sparse autoencoder in Automated Detection of Glaucoma in Fundus Images

Application of Stacked Sparse Autoencoder in Automated Detec...

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Conference on Unconventional Optical Imaging

作者： Pratiher, Sawon Chattoraj, Subhankar Vishwakarma, Karan Indian Inst Technol Kharagpur Kharagpur WB India Techno India Univ Salt Lake W Bengal India

ISBN: (数字)9781510618817

ISBN: (纸本)9781510618817

In this contribution, intelligent identification of glaucoma from digital fundus images using stacked sparse autoencoder (SSAE) is proposed. The fundus images are initially converted to gray-scale and normalized w.r.t., background illuminance while maintaining contrast constancy across the dataset. Unfolded feature vectors from the pre-processed with proper rescaling and grays-scale converted fundus images are fed to SSAE for learning efficient feature representation and classification thereof using a softmax layer. A comparative evaluation highlighting the superiority of SSAE method with existing state-of the art techniques is presented to validate its efficacy in glaucoma detection. The proposed framework can be used as a clinical decision support system assisting ophthalmologists in confirming their diagnosis with high reliability & accuracy.

关键词： Medical imaging Fundus image Glaucoma stacked sparse autoencoder

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Vertebrae Segmentation via stacked sparse autoencoder from Computed Tomography Images 11

Vertebrae Segmentation via Stacked Sparse Autoencoder from C...

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11th International Conference on Digital Image Processing (ICDIP)

作者： Qadri, Syed Furqan Zhao, Zhiqi Ai, Danni Ahmad, Mubashir Wang, Yongtian Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Beijing Inst Technol Sch Opt & Photon Beijing Engn Res Ctr Mixed Real & Adv Display Beijing 100081 Peoples R China AICFVE Beijing Film Acad 4 Xitucheng Rd Beijing 100088 Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

An accurate vertebrae segmentation in the spine is an essential pre-requisite in many applications of image-based spine assessment, surgical planning, clinical diagnostic treatment, and biomechanical modeling. In this paper, we present the stacked sparse autoencoder (SSAE) model for the segmentation of vertebrae from CT images. After the preprocessing step, we extracted overlapped patches from the vertebrae CT images as the inputs of our proposed model. The SSAE model was trained in an unsupervised way to learn high-level features from the input pixels of the unlabeled images patch. To improve the discriminability of the learned features, we further refined the feature representation in a supervised fashion and fine-tuned the whole model by using the feedforward neural network parameters for classifying the overlapped patches. We then validated our model on a publicly available MICCAI CSI2014 dataset and found that our model outperforms the other state-of-the-art methods.

关键词： Vertebrae segmentation deep learning stacked sparse autoencoder CT images

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PolSAR Marine Aquaculture Detection Based on Nonlocal stacked sparse autoencoder 16th

PolSAR Marine Aquaculture Detection Based on Nonlocal Stacke...

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16th International Symposium on Neural Networks (ISNN)

作者： Fan, Jianchao Liu, Xiaoxin Hu, Yuanyuan Han, Min Natl Marine Environm Monitoring Ctr Dept Ocean Remote Sensing Dalian 116023 Liaoning Peoples R China Washington Univ Comp Sci & Engn St Louis MO 63130 USA Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Liaoning Peoples R China

ISBN: (纸本)9783030228088;9783030228071

Marine aquaculture plays an important role in marine economic, which distributes widely around the coast. Using satellite remote sensing monitoring, it can achieve large scale dynamic monitoring. As a classic model of deep learning, stacked sparse autoencoder (SSAE) has the advantages of simple model and self-learning of features. Nonlocal spatial information is utilized to assist SSAE construct NSSAE to improve the precision in this paper. Experimental results demonstrate the superiority of nonlocal SSAE methods on marine target recognition.

关键词： Polarimetric SAR Remote sensing images Nonlocal spatial information stacked sparse autoencoder Classification

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Abnormal Condition Monitoring Based on stacked sparse autoencoder 1

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13th International Conference on Computer Engineering and Networks (CENet)

作者： Qiao, Lin Qu, Ruiting Song, Qing Liu, Biqi Liu, Xiaoqiang Liu, Yubo State Grid Liaoning Elect Power Co Ltd Informat & Commun Branch Shenyang Peoples R China

ISBN: (数字)9789819992430

ISBN: (纸本)9789819992454;9789819992430;9789819992423

In recent years, abnormal condition monitoring has provoked vast amount o attention and research from multiple disciplines. Especially in the microservice architecture based on the cloud platform, abnormal condition monitoring has emerged as a powerful instrument for improving the stability of service. However, along with the development of geometric growth in the number of microservice, due to the lack of systematic methods, it is difficult to effectively analyze and extract valuable information from the large amount of data collected, the abnormal monitoring in cloud platform O&M is facing new difficulties and challenges. Traditional operation and maintenance work mainly relies on human experience to analyze a large number of indicators to determine whether there is a failure, which is very inefficient and relies heavily on expert opinions. To address these problems, in this paper, we propose a abnormal condition monitoring method based on stacked sparse autoencoder for cloud platform. Our method aims to model the operating state based on the historical experience with deep autoencoder, then the constructed model is used to analyze the current status of the service and predict the possible abnormal condition. Experimental results on real-world datasets demonstrate the effectiveness of our method compared to the traditional methods.

关键词： Abnormal condition monitoring stacked sparse autoencoder Intelligent operation and maintenance

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A NONINVASIVE METHOD TO DETECT DIABETES MELLITUS AND LUNG CANCER USING THE stacked sparse autoencoder

A NONINVASIVE METHOD TO DETECT DIABETES MELLITUS AND LUNG CA...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhang, Qi Zhou, Jianhang Zhang, Bob Univ Macau Dept Comp & Informat Sci PAMI Res Grp Taipa Macau Peoples R China

ISBN: (数字)9781509066315

ISBN: (纸本)9781509066315

Diabetes mellitus and lung cancer are two of the most common fatal diseases in the world, causing considerable deaths every year. However, it is not easy to detect diabetes mellitus and lung cancer efficiently-needing professional medical instruments such as a CT and a qualified individual to perform the Fasting Plasma Glucose test. Considering the risks and various inconveniences with conventional diagnosis methods, noninvasive approaches based on computerized analysis are desired. The aim of this paper is to distinguish patients with diabetes mellitus, lung cancer from healthy people simultaneously by analyzing facial images through the stacked sparse autoencoder. Experimental results on a dataset containing 450 healthy samples, 284 diabetes and 175 lung cancer patients produced the F1-score of 93.57%, 97.54%, 81.56% for detecting healthy, diabetes and lung cancer, respectively, validating the effectiveness of our proposed method.

关键词： Diabetes mellitus lung cancer facial image medical biometrics stacked sparse autoencoder

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